Precipation-2D

import intake
from easygems import healpix as egh
import easygems.remap as egr
import healpy as hp

import matplotlib.pyplot as plt
import warnings

warnings.filterwarnings("ignore", category=FutureWarning) # don't warn us about future package conflicts

open dataset

current_location = "NCAR" #"online"
cat = intake.open_catalog("https://digital-earths-global-hackathon.github.io/catalog/catalog.yaml")[current_location]
list(cat)

['CERES_EBAF',
 'ERA5',
 'IR_IMERG',
 'JRA3Q',
 'MERRA2',
 'casesm2_10km_nocumulus',
 'icon_d3hp003',
 'icon_d3hp003aug',
 'icon_d3hp003feb',
 'icon_ngc4008',
 'ifs_tco3999-ng5_deepoff',
 'ifs_tco3999-ng5_rcbmf',
 'ifs_tco3999-ng5_rcbmf_cf',
 'mpas_dyamond1',
 'mpas_dyamond2',
 'mpas_dyamond3',
 'nicam_220m_test',
 'nicam_gl11',
 'scream-dkrz',
 'scream2D_hrly',
 'scream_lnd',
 'scream_ne120',
 'tracking-d3hp003',
 'um_Africa_km4p4_RAL3P3_n1280_GAL9_nest',
 'um_CTC_km4p4_RAL3P3_n1280_GAL9_nest',
 'um_SAmer_km4p4_RAL3P3_n1280_GAL9_nest',
 'um_SEA_km4p4_RAL3P3_n1280_GAL9_nest',
 'um_glm_n1280_CoMA9_TBv1p2',
 'um_glm_n1280_GAL9',
 'um_glm_n2560_RAL3p3',
 'wrf_conus',
 'wrf_samerica']

import pandas as pd

pd.DataFrame(cat["mpas_dyamond1"].describe()["user_parameters"])

	name	description	type	allowed	default
0	time	temporal resolution of the dataset	str	[PT15M, PT3H]	PT3H
1	zoom	zoom resolution of the dataset	int	[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]	7

select variable

scream = cat['scream2D_hrly'](zoom = 7).to_dask()
scream

<xarray.Dataset> Size: 15GB
Dimensions:   (crs: 0, time: 9457, cell: 196608)
Coordinates:
  * crs       (crs) float64 0B 
  * time      (time) object 76kB 2019-08-01 00:00:00 ... 2020-09-01 00:00:00
Dimensions without coordinates: cell
Data variables:
    pr        (time, cell) float32 7GB dask.array<chunksize=(24, 196608), meta=np.ndarray>
    rlut      (time, cell) float32 7GB dask.array<chunksize=(24, 196608), meta=np.ndarray>
    ELEV      (cell) float32 786kB dask.array<chunksize=(4096,), meta=np.ndarray>
    LANDFRAC  (cell) float32 786kB dask.array<chunksize=(4096,), meta=np.ndarray>
    lat       (cell) float32 786kB dask.array<chunksize=(4096,), meta=np.ndarray>
    lon       (cell) float32 786kB dask.array<chunksize=(4096,), meta=np.ndarray>
Attributes: (12/22)
    Conventions:                  CF-1.8
    atm_initial_conditions_file:  /lustre/orion/cli115/world-shared/e3sm/inpu...
    averaging_frequency:          1
    averaging_frequency_units:    nhours
    averaging_type:               INSTANT
    case:                         cess-control.ne1024pg2_ne1024pg2.F2010-SCRE...
    ...                           ...
    product:                      model-output
    realm:                        atmos
    run_t0:                       2019-08-01-00000
    source:                       E3SM Atmosphere Model (EAMxx)
    topography_file:              /lustre/orion/cli115/world-shared/e3sm/inpu...
    username:                     noel

xarray.Dataset

• Dimensions:
  • crs: 0
  • time: 9457
  • cell: 196608
• Coordinates: (2)
  • crs
    (crs)
    float64

    grid_mapping_name :

    healpix

    healpix_nside :

    128

    healpix_order :

    nest

    array([], dtype=float64)

  • time
    (time)
    object
    2019-08-01 00:00:00 ... 2020-09-...

    long_name :

    time

    array([cftime.DatetimeNoLeap(2019, 8, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2019, 8, 1, 1, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2019, 8, 1, 2, 0, 0, 0, has_year_zero=True), ...,
           cftime.DatetimeNoLeap(2020, 8, 31, 22, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 8, 31, 23, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 9, 1, 0, 0, 0, 0, has_year_zero=True)],
          shape=(9457,), dtype=object)

• Data variables: (6)
  • pr
    (time, cell)
    float32
    dask.array<chunksize=(24, 196608), meta=np.ndarray>

    long_name :

    precip_total_surf_mass_flux

    units :

    m s^-1

    Array Chunk Bytes 6.93 GiB 18.00 MiB Shape (9457, 196608) (24, 196608) Dask graph 395 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rlut
    (time, cell)
    float32
    dask.array<chunksize=(24, 196608), meta=np.ndarray>

    long_name :

    LW_flux_up_at_model_top

    units :

    W/m2

    Array Chunk Bytes 6.93 GiB 18.00 MiB Shape (9457, 196608) (24, 196608) Dask graph 395 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ELEV
    (cell)
    float32
    dask.array<chunksize=(4096,), meta=np.ndarray>

    long_name :

    Surface elevation

    standard_name :

    surface_elevation

    units :

    m

    Array Chunk Bytes 768.00 kiB 16.00 kiB Shape (196608,) (4096,) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray

  • LANDFRAC
    (cell)
    float32
    dask.array<chunksize=(4096,), meta=np.ndarray>

    long_name :

    gridbox land fraction

    Array Chunk Bytes 768.00 kiB 16.00 kiB Shape (196608,) (4096,) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lat
    (cell)
    float32
    dask.array<chunksize=(4096,), meta=np.ndarray>

    long_name :

    latitude

    units :

    degrees_north

    Array Chunk Bytes 768.00 kiB 16.00 kiB Shape (196608,) (4096,) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lon
    (cell)
    float32
    dask.array<chunksize=(4096,), meta=np.ndarray>

    long_name :

    longitude

    units :

    degrees_east

    Array Chunk Bytes 768.00 kiB 16.00 kiB Shape (196608,) (4096,) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (2)
  • crs
    PandasIndex

    PandasIndex(Index([], dtype='float64', name='crs'))

  • time
    PandasIndex

    PandasIndex(CFTimeIndex([2019-08-01 00:00:00, 2019-08-01 01:00:00, 2019-08-01 02:00:00,
                 2019-08-01 03:00:00, 2019-08-01 04:00:00, 2019-08-01 05:00:00,
                 2019-08-01 06:00:00, 2019-08-01 07:00:00, 2019-08-01 08:00:00,
                 2019-08-01 09:00:00,
                 ...
                 2020-08-31 15:00:00, 2020-08-31 16:00:00, 2020-08-31 17:00:00,
                 2020-08-31 18:00:00, 2020-08-31 19:00:00, 2020-08-31 20:00:00,
                 2020-08-31 21:00:00, 2020-08-31 22:00:00, 2020-08-31 23:00:00,
                 2020-09-01 00:00:00],
                dtype='object', length=9457, calendar='noleap', freq=None))

• Attributes: (22)

  Conventions :

  CF-1.8

  atm_initial_conditions_file :

  /lustre/orion/cli115/world-shared/e3sm/inputdata/atm/scream/init/screami_ne1024np4L128_era5-20190801-topoadjx6t_20230620.nc

  averaging_frequency :

  1

  averaging_frequency_units :

  nhours

  averaging_type :

  INSTANT

  case :

  cess-control.ne1024pg2_ne1024pg2.F2010-SCREAMv1.cess-oct2

  case_t0 :

  2019-08-01-00000

  contact :

  e3sm-data-support@llnl.gov

  eamxx_version :

  1.0.0

  fp_precision :

  single

  git_version :

  f4f0672bb4283cad9347808e9576859974fffb12

  history :

  created on Sun Oct 8 17:20:09 2023

  hostname :

  frontier-scream-gpu

  institution_id :

  E3SM-Projet

  institutions :

  LLNL (Lawrence Livermore National Laboratory, Livermore, CA 94550, USA); ANL (Argonne National Laboratory, Argonne, IL 60439, USA); BNL (Brookhaven National Laboratory, Upton, NY 11973, USA); LANL (Los Alamos National Laboratory, Los Alamos, NM 87545, USA); LBNL (Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA); ORNL (Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA); PNNL (Pacific Northwest National Laboratory, Richland, WA 99352, USA); SNL (Sandia National Laboratories, Albuquerque, NM 87185, USA). Mailing address: LLNL Climate Program, c/o David C. Bader, Principal Investigator, L-103, 7000 East Avenue, Livermore, CA 94550, USA

  max_snapshots_per_file :

  96

  product :

  model-output

  realm :

  atmos

  run_t0 :

  2019-08-01-00000

  source :

  E3SM Atmosphere Model (EAMxx)

  topography_file :

  /lustre/orion/cli115/world-shared/e3sm/inputdata/atm/cam/topo/USGS-gtopo30_ne1024np4pg2_x6t-SGH.c20210614.nc

  username :

  noel

scream_precip = scream.pr
scream_precip
scream_precip_mmhr = scream_precip * 36e5

plot 2D

import cftime 
start_time = cftime.DatetimeNoLeap(2020, 6, 1, 0, 0, 0, 0)
end_time = cftime.DatetimeNoLeap(2020, 6, 30, 0, 0, 0, 0)
scream_precip_june =  scream_precip_mmhr.sel(time=slice(start_time, end_time)).mean(dim="time").compute()

def get_NCL_colormap(cmap_name, extend='None'):
    '''Read an NCL RGB colormap file and convert it to a Matplotlib colormap
    object.

    Parameter:
        cmap_name     NCL RGB colormap name, e.g. 'ncl_default'
        extend        use NCL behavior of color handling for the colorbar 'under'
                      and 'over' colors. 'None' or 'ncl', default 'None'

    Description:
        Read the NCL colormap and convert it to a Matplotlib Colormap object.
        It checks if the colormap file is already available or use the
        appropriate URL.

        If NCL is installed the colormap will be searched in its colormaps
        folder $NCARG_ROOT/lib/ncarg/colormaps.

        Returns a Matplotlib Colormap object.
    '''
    from matplotlib.colors import ListedColormap
    import requests
    import errno
    import os, re
    import numpy as np

    #-- NCL colormaps URL
    NCARG_URL = 'https://www.ncl.ucar.edu/Document/Graphics/ColorTables/Files/'

    #-- read the NCL colormap RGB file
    colormap_file = cmap_name+'.rgb'
    cfile = os.path.split(colormap_file)[1]

    if os.path.isfile(colormap_file) == False:
        #-- if NCL is already installed
        if 'NCARG_ROOT' in os.environ:
            cpath = os.environ['NCARG_ROOT']+'/lib/ncarg/colormaps/'
            if os.path.isfile(cpath + cfile):
                colormap_file = cpath + cfile
                with open(colormap_file) as f:
                    lines = [re.sub('\s+',' ',l)  for l in f.read().splitlines() if not (l.startswith('#') or l.startswith('n'))]
        #-- use URL to read colormap
        elif not 'NCARG_ROOT' in os.environ:
            url_file = NCARG_URL+'/'+cmap_name+'.rgb'
            res = requests.head(url_file)
            if not res.status_code == 200:
                print(f'{cmap_name} does not exist!')
                raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), cmap_name)
            content = requests.get(url_file, stream=True).content
            lines = [re.sub('\s+',' ',l)  for l in content.decode('UTF-8').splitlines() if not (l.startswith('#') or l.startswith('n'))]
        else:
            raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), colormap_file)
    #-- use local colormap file
    else:
        with open(colormap_file) as f:
            lines = [re.sub('\s+',' ',l)  for l in f.read().splitlines() if not (l.startswith('#') or l.startswith('n'))]

    #-- skip all possible header lines
    tmp  = [l.split('#', 1)[0] for l in lines]

    tmp = [re.sub(r'\s+',' ', s) for s in tmp]
    tmp = [ x for x in tmp if ';' not in x ]
    tmp = [ x for x in tmp if x != '']

    #-- get the RGB values
    i = 0
    for l in tmp:
        new_array = np.array(l.split()).astype(float)
        if i == 0:
            color_list = new_array
        else:
            color_list = np.vstack((color_list, new_array))
        i += 1

    #-- make sure that the RGB values are within range 0 to 1
    if (color_list > 1.).any(): color_list = color_list / 255

    #-- add alpha-channel RGB -> RGBA
    alpha        = np.ones((color_list.shape[0],4))
    alpha[:,:-1] = color_list
    color_list   = alpha

    #-- convert to Colormap object
    if extend == 'ncl':
        cmap = ListedColormap(color_list[1:-1,:])
    else:
        cmap = ListedColormap(color_list)

    #-- define the under, over, and bad colors
    under = color_list[0,:]
    over  = color_list[-1,:]
    bad   = [0.5, 0.5, 0.5, 1.]
    cmap.set_extremes(under=color_list[0], bad=bad, over=color_list[-1])

    return cmap

<>:40: SyntaxWarning: invalid escape sequence '\s'
<>:49: SyntaxWarning: invalid escape sequence '\s'
<>:55: SyntaxWarning: invalid escape sequence '\s'
<>:40: SyntaxWarning: invalid escape sequence '\s'
<>:49: SyntaxWarning: invalid escape sequence '\s'
<>:55: SyntaxWarning: invalid escape sequence '\s'
/glade/derecho/scratch/weiyiwang/tmp/ipykernel_29936/248976764.py:40: SyntaxWarning: invalid escape sequence '\s'
  lines = [re.sub('\s+',' ',l)  for l in f.read().splitlines() if not (l.startswith('#') or l.startswith('n'))]
/glade/derecho/scratch/weiyiwang/tmp/ipykernel_29936/248976764.py:49: SyntaxWarning: invalid escape sequence '\s'
  lines = [re.sub('\s+',' ',l)  for l in content.decode('UTF-8').splitlines() if not (l.startswith('#') or l.startswith('n'))]
/glade/derecho/scratch/weiyiwang/tmp/ipykernel_29936/248976764.py:55: SyntaxWarning: invalid escape sequence '\s'
  lines = [re.sub('\s+',' ',l)  for l in f.read().splitlines() if not (l.startswith('#') or l.startswith('n'))]

cmp=get_NCL_colormap('WhiteBlueGreenYellowRed')

plot using imshow

import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cf
import cmocean

# Example projection
projection = ccrs.Robinson(central_longitude=180)
fs = 24

# Create figure with 2 subplots
fig, axes = plt.subplots(
    ncols=1,
    figsize=(14, 5),
    subplot_kw={"projection": projection},
    constrained_layout=True
)

axes.set_global()
fg = egh.healpix_show(scream_precip_june, ax=axes, cmap=cmp, vmin=.1, vmax=.6)#cmocean.cm.thermal, vmax=0.5)
axes.add_feature(cf.COASTLINE, linewidth=0.8)
axes.add_feature(cf.BORDERS, linewidth=0.4)
axes.set_title("SCREAM Precip (zoom=7) June 2020", fontsize = fs )
fig.colorbar(fg)
plt.show()

# Create figure with 2 subplots
fig, axes = plt.subplots(
    ncols=2,
    figsize=(14, 5),
    subplot_kw={"projection": projection},
    constrained_layout=True
)

# Set same extent for both plots
extent = [90, 124, 20, 55]

# First subplot
axes[0].set_global()
axes[1].set_extent(extent, crs=ccrs.PlateCarree())
egh.healpix_show(scream_precip_june, ax=axes[0], cmap=cmp, vmin=.1, vmax=.6)
axes[0].add_feature(cf.COASTLINE, linewidth=0.8)
axes[0].add_feature(cf.BORDERS, linewidth=0.4)
#axes[0].set_title("SCREAM", fontsize = fs )
fg = egh.healpix_show(scream_precip_june, ax=axes[1], cmap=cmp, vmin=.1, vmax=.6)#cmocean.cm.thermal, vmax=0.5)
axes[1].add_feature(cf.COASTLINE, linewidth=0.8)
axes[1].add_feature(cf.BORDERS, linewidth=0.4)
fig.colorbar(fg)
plt.show()

plot with contour

projection = ccrs.Robinson(central_longitude=180)
fs = 24

# Create figure with 2 subplots
fig, axes = plt.subplots(
    ncols=1,
    figsize=(14, 5),
    subplot_kw={"projection": projection},
    constrained_layout=True
)

extent = [90, 124, 20, 55]
axes.set_extent(extent, crs=ccrs.PlateCarree())
fg = egh.healpix_show(scream_precip_june, ax=axes, cmap=cmp, vmin=.1, vmax=.6, method="linear")
cn = egh.healpix_contour(scream_precip_june, ax=axes, levels=[0.3,0.6], colors="k", linewidths=1, alpha=1)
axes.add_feature(cf.BORDERS, linewidth=0.8)
axes.add_feature(cf.COASTLINE, linewidth=0.8)
axes.set_title("SCREAM Precip (zoom=7) June 2020", fontsize = fs )
fig.colorbar(fg)
plt.clabel(cn, inline=True, fontsize=8, colors="k", fmt="%0.1f")
plt.show()

plot using UXarray

import uxarray as ux

uxds = ux.UxDataset.from_healpix(scream)
uxds

<xarray.UxDataset> Size: 15GB
Dimensions:   (crs: 0, time: 9457, n_face: 196608)
Coordinates:
  * crs       (crs) float64 0B 
  * time      (time) object 76kB 2019-08-01 00:00:00 ... 2020-09-01 00:00:00
Dimensions without coordinates: n_face
Data variables:
    pr        (time, n_face) float32 7GB dask.array<chunksize=(24, 196608), meta=np.ndarray>
    rlut      (time, n_face) float32 7GB dask.array<chunksize=(24, 196608), meta=np.ndarray>
    ELEV      (n_face) float32 786kB dask.array<chunksize=(4096,), meta=np.ndarray>
    LANDFRAC  (n_face) float32 786kB dask.array<chunksize=(4096,), meta=np.ndarray>
    lat       (n_face) float32 786kB dask.array<chunksize=(4096,), meta=np.ndarray>
    lon       (n_face) float32 786kB dask.array<chunksize=(4096,), meta=np.ndarray>

<xarray.UxDataset> Size: 15GB
Dimensions:   (crs: 0, time: 9457, n_face: 196608)
Coordinates:
  * crs       (crs) float64 0B 
  * time      (time) object 76kB 2019-08-01 00:00:00 ... 2020-09-01 00:00:00
Dimensions without coordinates: n_face
Data variables:
    pr        (time, n_face) float32 7GB dask.array<chunksize=(24, 196608), meta=np.ndarray>
    rlut      (time, n_face) float32 7GB dask.array<chunksize=(24, 196608), meta=np.ndarray>
    ELEV      (n_face) float32 786kB dask.array<chunksize=(4096,), meta=np.ndarray>
    LANDFRAC  (n_face) float32 786kB dask.array<chunksize=(4096,), meta=np.ndarray>
    lat       (n_face) float32 786kB dask.array<chunksize=(4096,), meta=np.ndarray>
    lon       (n_face) float32 786kB dask.array<chunksize=(4096,), meta=np.ndarray>

uxarray.UxDataset

• Dimensions:
  • crs: 0
  • time: 9457
  • n_face: 196608
• Coordinates: (2)
  • crs
    (crs)
    float64

    grid_mapping_name :

    healpix

    healpix_nside :

    128

    healpix_order :

    nest

    array([], dtype=float64)

  • time
    (time)
    object
    2019-08-01 00:00:00 ... 2020-09-...

    long_name :

    time

    array([cftime.DatetimeNoLeap(2019, 8, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2019, 8, 1, 1, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2019, 8, 1, 2, 0, 0, 0, has_year_zero=True), ...,
           cftime.DatetimeNoLeap(2020, 8, 31, 22, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 8, 31, 23, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2020, 9, 1, 0, 0, 0, 0, has_year_zero=True)],
          shape=(9457,), dtype=object)

• Data variables: (6)
  • pr
    (time, n_face)
    float32
    dask.array<chunksize=(24, 196608), meta=np.ndarray>

    long_name :

    precip_total_surf_mass_flux

    units :

    m s^-1

    Array Chunk Bytes 6.93 GiB 18.00 MiB Shape (9457, 196608) (24, 196608) Dask graph 395 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rlut
    (time, n_face)
    float32
    dask.array<chunksize=(24, 196608), meta=np.ndarray>

    long_name :

    LW_flux_up_at_model_top

    units :

    W/m2

    Array Chunk Bytes 6.93 GiB 18.00 MiB Shape (9457, 196608) (24, 196608) Dask graph 395 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ELEV
    (n_face)
    float32
    dask.array<chunksize=(4096,), meta=np.ndarray>

    long_name :

    Surface elevation

    standard_name :

    surface_elevation

    units :

    m

    Array Chunk Bytes 768.00 kiB 16.00 kiB Shape (196608,) (4096,) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray

  • LANDFRAC
    (n_face)
    float32
    dask.array<chunksize=(4096,), meta=np.ndarray>

    long_name :

    gridbox land fraction

    Array Chunk Bytes 768.00 kiB 16.00 kiB Shape (196608,) (4096,) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lat
    (n_face)
    float32
    dask.array<chunksize=(4096,), meta=np.ndarray>

    long_name :

    latitude

    units :

    degrees_north

    Array Chunk Bytes 768.00 kiB 16.00 kiB Shape (196608,) (4096,) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lon
    (n_face)
    float32
    dask.array<chunksize=(4096,), meta=np.ndarray>

    long_name :

    longitude

    units :

    degrees_east

    Array Chunk Bytes 768.00 kiB 16.00 kiB Shape (196608,) (4096,) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (2)
  • crs
    PandasIndex

    PandasIndex(Index([], dtype='float64', name='crs'))

  • time
    PandasIndex

    PandasIndex(CFTimeIndex([2019-08-01 00:00:00, 2019-08-01 01:00:00, 2019-08-01 02:00:00,
                 2019-08-01 03:00:00, 2019-08-01 04:00:00, 2019-08-01 05:00:00,
                 2019-08-01 06:00:00, 2019-08-01 07:00:00, 2019-08-01 08:00:00,
                 2019-08-01 09:00:00,
                 ...
                 2020-08-31 15:00:00, 2020-08-31 16:00:00, 2020-08-31 17:00:00,
                 2020-08-31 18:00:00, 2020-08-31 19:00:00, 2020-08-31 20:00:00,
                 2020-08-31 21:00:00, 2020-08-31 22:00:00, 2020-08-31 23:00:00,
                 2020-09-01 00:00:00],
                dtype='object', length=9457, calendar='noleap', freq=None))

• Attributes: (0)

Show Grid Information

<uxarray.Grid>
Original Grid Type: HEALPix
Grid Dimensions:
  * n_face: 196608
Grid Coordinates (Spherical):
  * face_lon: (196608,)
  * face_lat: (196608,)
Grid Coordinates (Cartesian):
Grid Connectivity Variables:
Grid Descriptor Variables:

uxarray.UxDataset.uxgrid

• Dimensions:
  • n_face: 196608
• Spherical Coordinates: (2)
  • face_lon
    (n_face)
    float64
    45.0 45.35 44.65 ... -45.35 -45.0

    standard_name :

    longitude

    long name :

    Longitude of the center of each face

    units :

    degrees_east

    array([ 45.       ,  45.3515625,  44.6484375, ..., -44.6484375,
           -45.3515625, -45.       ], shape=(196608,))

  • face_lat
    (n_face)
    float64
    0.2984 0.5968 ... -0.5968 -0.2984

    standard_name :

    latitude

    long name :

    Latitude of the center of each face

    units :

    degrees_north

    array([ 0.29841687,  0.59684183,  0.59684183, ..., -0.59684183,
           -0.59684183, -0.29841687], shape=(196608,))

• Cartesian Coordinates: (0)
• Connectivity: (0)
• Descriptors: (0)
• Attributes: (5)

  zoom :

  7

  n_side :

  128

  n_pix :

  196608

  nest :

  True

  source_grid_spec :

  HEALPix

scream_precip = uxds.pr
scream_precip
scream_precip_mmhr = scream_precip * 36e5

start_time = cftime.DatetimeNoLeap(2020, 6, 1, 0, 0, 0, 0)
end_time = cftime.DatetimeNoLeap(2020, 6, 30, 0, 0, 0, 0)
scream_precip_june =  scream_precip_mmhr.sel(time=slice(start_time, end_time)).mean(dim="time").compute()

projection = ccrs.Robinson(central_longitude=180)

scream_precip_june.plot(
    projection=projection,
    cmap=cmp,
    features=["borders", "coastline"],
    title="SCREAM Precip (zoom=7) June 2020",
    width=700,
    clim=(0.1,0.6)
)

WARNING:param.GeoOverlayPlot01127: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.

lon_bounds = (80, 124)
lat_bounds = (20, 55)

scream_precip_june_subset = scream_precip_june.subset.bounding_box(lon_bounds, lat_bounds)
#scream_precip_june_subset = scream_precip_mmhr.sel(time=slice(start_time, end_time)).subset.bounding_box(lon_bounds, lat_bounds).mean(dim="time").compute()

projection = ccrs.Robinson(central_longitude=180)

scream_precip_june_subset.plot(
    projection=projection,
    cmap=cmp,
    features=["borders", "coastline"],
    title="SCREAM Precip (zoom=7) June 2020",
    width=1100,
    clim=(0.1,0.6)
)

WARNING:param.GeoOverlayPlot03029: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.